adaptation

Downscaling Climate Data

Downscaling climate data is a strategy for generating locally
relevant data from Global Circulation Models (GCMs). The overarching strategy is to connect global
scale predictions and regional dynamics to generate regionally specific
forecasts. Downscaling can be done in several ways.

Nesting a regional climate model into an existing GCM is one way to
downscale data. To do this, a specific location is defined and certain
driving factors from the GCM are applied to the regional climate model.
A regional climate model is a dynamic model, like a GCM, but it can be
thought of as being composed of three layers. One layer is largely
driven by the GCM, another layer builds on some locally specific data,
and the third layer uses its own physics based equations to resolve the
model based on data from the other two. The results are comparatively
local predictions that are informed by both local specifics and global
models. This process requires significant computational resources
because it is dependent on the use of complex models. Currently
Canada has just one Regional Climate Model (CRCM).

A second way of downscaling climate data is through the use of
statistical regressions. There are a variety of such methods ranging
from multiple regressions that link local variables to particular
drivers in GCMs, to more complex methods using statistics designed for
neural networks. The general strategy of these methods is to establish
the relationship between large scale variables, such as the driving
factors derived from GCMs, to local level climate conditions. Once these
relationships have been developed for existing conditions, they can be
used to predict what might happen under the different conditions
indicated by GCMs.

A third strategy for downscaling data is also statistically driven (and
thus not dynamic like a regional climate model). This strategy uses stochastic weather
generators. The weather generator develops a series of statistical
linkages among variables to predict weather at that particular location
by using long term weather data for a particular area. These empirically
based models can be used to downscale data by using data, such as wind
speed or other variables, generated from GCMs to predict the local
result of driving variables.

All of these techniques are estimations, but they can generate useful local data. In the Canadian context, a range of
climate data, both directly generated from GCMs and downscaled, is
available for decision makers.